After collecting idealized simulation ranges, derived ranges or both as described in Collect Ranges, propose data types for objects in your model based on the collected ideal ranges.
|Optimize data types of a system|
|Create fixed-point converter object|
|Get a list of subsystems to replace with an approximation|
|Proposal settings object for data type proposals|
|Specify options for data type optimization|
|Optimized fixed-point implementation of system|
|Result after optimizing fixed-point system|
Convert data types in your model to fixed point in one of three ways.
Use the Fixed-Pint Tool to convert a system from floating point to fixed point.
Third step in autoscaling workflow.
Use the Fixed-Point Tool to convert a floating-point model to fixed point.
Use the Fixed-Point Tool to scale fixed-point data types in a feedback model.
The Fixed-Point Tool proposes data types based on collected ranges and proposal settings.
This example shows how to use the Fixed-Point Tool to propose word lengths for a model that implements a simple moving average algorithm.
Use the Fixed-Point Tool to merge results from multiple simulations, and propose data types based on the merged results.
Blocks can inherit data types from a variety of sources. You can get data type proposals for blocks that use inherited output data types using the Fixed-Point Tool.
Highlight the differences between the command-line interface workflow and the Fixed-Point Tool workflow
Use the command line interface of the Fixed-Point Tool to autoscale a model.
Optimize data types in a system based on specified tolerances.
Define multiple simulation scenarios for range collection and verification.
The Fixed-Point Tool logs simulation minimum and maximum values for referenced models and proposes data types based on a union of the collected ranges.
Use the Fixed-Point Tool to convert a model that uses data objects for data type specification to fixed point.
Use the Fixed-Point Tool to convert a MATLAB® Function block to fixed-point.
Automatically replace functions in a MATLAB Function Block with a lookup table replacement.
Investigate errors thrown during data type optimization using the
Overview of modeling practices that could cause data type propagation errors after autoscaling.
Under certain conditions, the Fixed-Point Tool may propose a data type that is not compatible with the model.
This section explains what to do when the Fixed-Point Tool does not propose any data types.
This example shows how to replace a structure initial condition